Despite progress in Arabic large language models, such as Jais and AceGPT, their evaluation on commonsense reasoning has largely relied on machine-translated datasets, which lack cultural depth and may introduce Anglocentric biases. Commonsense reasoning is shaped by geographical and cultural contexts, and existing English datasets fail to capture the diversity of the Arab world. To address this, we introduce , a commonsense reasoning dataset in Modern Standard Arabic (MSA), covering cultures of 13 countries across the Gulf, Levant, North Africa, and the Nile Valley. The dataset was built from scratch by engaging native speakers to write and validate culturally relevant questions for their respective countries. spans 12 daily life domains with 54 fine-grained subtopics, reflecting various aspects of social norms, traditions, and everyday experiences. Zero-shot evaluations show that open-weight language models with up to 32B parameters struggle to comprehend diverse Arab cultures, with performance varying across regions. These findings highlight the need for more culturally aware models and datasets tailored to the Arabic-speaking world.
@inproceedings{sadallah-etal-2025-commonsense,title={Commonsense Reasoning in {A}rab Culture},author={Sadallah, Abdelrahman and Tonga, Junior Cedric and Almubarak, Khalid and Almheiri, Saeed and Atif, Farah and Qwaider, Chatrine and Kadaoui, Karima and Shatnawi, Sara and Alesh, Yaser and Koto, Fajri},editor={Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},month=jul,year={2025},address={Vienna, Austria},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2025.acl-long.380/},pages={7695--7710},isbn={979-8-89176-251-0},}